Body mass is a key predictor of the health of an individual and the longevity of a population (Clements & Ozgul 2016, Cerini et al. 2023), as such understanding what impacts the body mass of an individual is a key goal in conservation biology if we are to maintain healthy wildlife populations into the future.
This is particular true for ungulate species in Africa which play a vital role in maintaining ecosystems by influencing vegetation structure, nutrient cycling, and predator-prey dynamics. Through their grazing and browsing behaviors, they help control plant growth, prevent bush encroachment, and promote biodiversity. Their dung enriches soil fertility, supporting plant regeneration and insect populations. Additionally, ungulates serve as key prey for large carnivores, maintaining the balance of predator populations. Seasonal migrations also shape ecosystem processes over large landscapes. Without wild ungulates, many ecosystems would degrade, losing their resilience, productivity, and the intricate web of life they support, suggesting that maintaining their populations is crucial for ecological stability in Africa.
It has been proposed that the survival of individuals, and thus the persistence of the population, is highly reliant on the body mass of the individuals in the population. However, we as yet do not have a clear understanding of the factors which drive changes in body mass in wild African ungulate populations. Your task is to determine what factors are the key drivers of body mass in your data, whilst accounting for any factors which may systematically bias your results.
Each of you will receive an unique data set on the body mass of one of the wild African ungulates (Giraffe, Elephant, Buffalo, Zebra, Wildebeest, Kudu). These data are downloadable here and also include other potential variables of interest:
You need to demonstrate what the determinants of body mass in your wild ungulate species are, using any statistical modelling frameworks you think are appropriate. You must write a report showing:
Please ensure you show all of the code you used to make these calculations and graphics, and note that the data provided to each of you is different to everyone else’s data, both in terms of the structure and what (if any) effects may be significant.
This report should be written in the style of the workbooks you are familiar with from the R course, and must contain no more than 5000 words (including all code, code comments, and explanatory text). The report should demonstrate the skills and best practices you have learnt over the Programming in R course: demonstrating your ability to write precise and concise R code, and to present your results in a format which other people can understand and check (you may want to use the Rmarkdown workbooks from the course as inspiration). Where appropriate you should include and interpret the outputs of statistical models so the reader can judge whether you have fitted appropriate models and whether your conclusions are justified.
Submission should be in the form of a .zip document containing both the rmarkdown file and the html output.
The deadline for this coursework is the 19th of December 2025 at 13:00.